Update trace.py tests / fix some warnings

cunumeric/array.py

@@ -770,11 +770,9 @@ def _convert_key(self, key, first=True):
             # Otherwise convert it to a cuNumeric array, check types
             # and get the thunk
             key = convert_to_cunumeric_ndarray(key)
-            if key.dtype != np.bool and not np.issubdtype(
-                key.dtype, np.integer
-            ):
+            if key.dtype != bool and not np.issubdtype(key.dtype, np.integer):
                 raise TypeError("index arrays should be int or bool type")
-            if key.dtype != np.bool and key.dtype != np.int64:
+            if key.dtype != bool and key.dtype != np.int64:
                 runtime.warn(
                     "converting index array to int64 type",
                     category=RuntimeWarning,

cunumeric/deferred.py

@@ -422,7 +422,7 @@ def _create_indexing_array(self, key, is_set=False):
         start_index = -1
         if (
             isinstance(key, NumPyThunk)
-            and key.dtype == np.bool
+            and key.dtype == bool
             and key.shape == rhs.shape
         ):
             if not isinstance(key, DeferredArray):
@@ -476,7 +476,7 @@ def _create_indexing_array(self, key, is_set=False):
                 transpose_needed = transpose_needed or ((dim - last_index) > 1)
                 if (
                     isinstance(k, NumPyThunk)
-                    and k.dtype == np.bool
+                    and k.dtype == bool
                     and k.ndim >= 2
                 ):
                     for i in range(dim, dim + k.ndim):
@@ -513,7 +513,7 @@ def _create_indexing_array(self, key, is_set=False):
             elif isinstance(k, NumPyThunk):
                 if not isinstance(key, DeferredArray):
                     k = self.runtime.to_deferred_array(k)
-                if k.dtype == np.bool:
+                if k.dtype == bool:
                     for i in range(k.ndim):
                         if k.shape[i] != store.shape[dim + i + shift]:
                             raise ValueError(

cunumeric/utils.py

@@ -97,7 +97,7 @@ def is_supported_dtype(dtype: Any) -> bool:
 
 
 def calculate_volume(shape: tuple[int, ...]) -> int:
-    if shape == ():
+    if len(shape) == 0:
         return 0
     return reduce(lambda x, y: x * y, shape)
 

tests/trace.py → tests/integration/test_trace.py

@@ -17,17 +17,14 @@
 from itertools import permutations
 
 import numpy as np
+import pytest
 from test_tools.generators import mk_seq_array
 
 import cunumeric as num
 from legate.core import LEGATE_MAX_DIM
 
 
-def test():
-    # --------------------------------------------------------------
-    # TRACE
-    # --------------------------------------------------------------
-
+def test_2d():
     a = np.arange(8).reshape((2, 4))
     a_num = num.array(a)
     res = np.trace(a)
@@ -38,6 +35,8 @@ def test():
     res_num = num.trace(a_num, dtype=float)
     assert np.array_equal(res, res_num)
 
+
+def test_3d():
     a = np.arange(8).reshape((2, 2, 2))
     a_num = num.array(a)
     res = np.trace(a)
@@ -62,26 +61,32 @@ def test():
     num.trace(a_num, dtype=int, out=out_num)
     assert np.array_equal(out, out_num)
 
+
+def test_4d():
     a = np.arange(24).reshape((2, 2, 2, 3))
     a_num = num.array(a)
     res = np.trace(a)
     res_num = num.trace(a_num)
     assert np.array_equal(res, res_num)
 
-    for ndim in range(2, LEGATE_MAX_DIM + 1):
-        a_shape = tuple(random.randint(1, 9) for i in range(ndim))
-        np_array = mk_seq_array(np, a_shape)
-        num_array = mk_seq_array(num, a_shape)
 
-        # test trace
-        for axes in permutations(range(ndim), 2):
-            diag_size = min(a_shape[axes[0]], a_shape[axes[1]]) - 1
-            for offset in range(-diag_size + 1, diag_size):
-                assert np.array_equal(
-                    np_array.trace(offset, axes[0], axes[1]),
-                    num_array.trace(offset, axes[0], axes[1]),
-                )
+@pytest.mark.parametrize("ndim", range(2, LEGATE_MAX_DIM + 1))
+def test_ndim(ndim):
+    a_shape = tuple(random.randint(1, 9) for i in range(ndim))
+    np_array = mk_seq_array(np, a_shape)
+    num_array = mk_seq_array(num, a_shape)
+
+    # test trace
+    for axes in permutations(range(ndim), 2):
+        diag_size = min(a_shape[axes[0]], a_shape[axes[1]]) - 1
+        for offset in range(-diag_size + 1, diag_size):
+            assert np.array_equal(
+                np_array.trace(offset, axes[0], axes[1]),
+                num_array.trace(offset, axes[0], axes[1]),
+            )
 
 
 if __name__ == "__main__":
-    test()
+    import sys
+
+    pytest.main(sys.argv)